Reinforcement Learning (RL) [1]} is a powerful optimisation method used for complex problems. In RL, an agent learns to perform a (set of) task(s) on the basis of how it has performed on the previous steps. The agent typically gets a reward for moving closer to the goal or the optimised value, and in some cases a punishment for deviating from its intended learning task. Reinforcement learning, in a lot of ways, is inspired by biological learning that generally happens in mammals; for e.g.: children learn a language by observing their environment and if they are able to mimic it well, they are rewarded with something in appreciation. Similar behaviour is also observed in animals, like dogs, which are given treats on successful completion of a task, say fetching a stick. Mammalian brain then tries and rewire itself so that it can perform certain actions which lead to successful completion of tasks and gives it some sort of short/long term rewards.
